"""simple docstring"""


import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin






class   __UpperCAmelCase		(      __lowerCAmelCase , unittest.TestCase      ):
				A__ :  int    				=			ProphetNetTokenizer
				A__ :  Optional[int]    				=			False

				def 						_a      ( self ):
										super().setUp()

										lowerCamelCase__   =[
										    "[UNK]",
										    "[CLS]",
										    "[SEP]",
										    "[PAD]",
										    "[MASK]",
										    "want",
										    "##want",
										    "##ed",
										    "wa",
										    "un",
										    "runn",
										    "##ing",
										    ",",
										    "low",
										    "lowest",
										]
										lowerCamelCase__   =os.path.join(self.tmpdirname      ,					VOCAB_FILES_NAMES["vocab_file"] )
										with open(self.vocab_file      ,					"w"      ,					encoding="utf-8" ) as vocab_writer:
																vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )

				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   ="UNwant\u00E9d,running"
										lowerCamelCase__   ="unwanted, running"
										return input_text, output_text

				def 						_a      ( self ):
										lowerCamelCase__   =self.tokenizer_class(self.vocab_file )

										lowerCamelCase__   =tokenizer.tokenize("UNwant\u00E9d,running" )
										self.assertListEqual(lowercase_      ,					["un", "##want", "##ed", ",", "runn", "##ing"] )
										self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_ )      ,					[9, 6, 7, 12, 10, 11] )

				def 						_a      ( self ):
										lowerCamelCase__   =BasicTokenizer()

										self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz" )      ,					["ah", "\u535A", "\u63A8", "zz"] )

				def 						_a      ( self ):
										lowerCamelCase__   =BasicTokenizer(do_lower_case=lowercase_ )

										self.assertListEqual(
										    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  " )      ,					["hello", "!", "how", "are", "you", "?"] )
										self.assertListEqual(tokenizer.tokenize("H\u00E9llo" )      ,					["hello"] )

				def 						_a      ( self ):
										lowerCamelCase__   =BasicTokenizer(do_lower_case=lowercase_      ,					strip_accents=lowercase_ )

										self.assertListEqual(
										    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  " )      ,					["hällo", "!", "how", "are", "you", "?"] )
										self.assertListEqual(tokenizer.tokenize("H\u00E9llo" )      ,					["h\u00E9llo"] )

				def 						_a      ( self ):
										lowerCamelCase__   =BasicTokenizer(do_lower_case=lowercase_      ,					strip_accents=lowercase_ )

										self.assertListEqual(
										    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  " )      ,					["hallo", "!", "how", "are", "you", "?"] )
										self.assertListEqual(tokenizer.tokenize("H\u00E9llo" )      ,					["hello"] )

				def 						_a      ( self ):
										lowerCamelCase__   =BasicTokenizer(do_lower_case=lowercase_ )

										self.assertListEqual(
										    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  " )      ,					["hallo", "!", "how", "are", "you", "?"] )
										self.assertListEqual(tokenizer.tokenize("H\u00E9llo" )      ,					["hello"] )

				def 						_a      ( self ):
										lowerCamelCase__   =BasicTokenizer(do_lower_case=lowercase_ )

										self.assertListEqual(
										    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  " )      ,					["HeLLo", "!", "how", "Are", "yoU", "?"] )

				def 						_a      ( self ):
										lowerCamelCase__   =BasicTokenizer(do_lower_case=lowercase_      ,					strip_accents=lowercase_ )

										self.assertListEqual(
										    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  " )      ,					["HäLLo", "!", "how", "Are", "yoU", "?"] )

				def 						_a      ( self ):
										lowerCamelCase__   =BasicTokenizer(do_lower_case=lowercase_      ,					strip_accents=lowercase_ )

										self.assertListEqual(
										    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  " )      ,					["HaLLo", "!", "how", "Are", "yoU", "?"] )

				def 						_a      ( self ):
										lowerCamelCase__   =BasicTokenizer(do_lower_case=lowercase_      ,					never_split=["[UNK]"] )

										self.assertListEqual(
										    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]" )      ,					["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"] )

				def 						_a      ( self ):
										lowerCamelCase__   =["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

										lowerCamelCase__   ={}
										for i, token in enumerate(lowercase_ ):
																lowerCamelCase__   =i
										lowerCamelCase__   =WordpieceTokenizer(vocab=lowercase_      ,					unk_token="[UNK]" )

										self.assertListEqual(tokenizer.tokenize("" )      ,					[] )

										self.assertListEqual(tokenizer.tokenize("unwanted running" )      ,					["un", "##want", "##ed", "runn", "##ing"] )

										self.assertListEqual(tokenizer.tokenize("unwantedX running" )      ,					["[UNK]", "runn", "##ing"] )

				@require_torch
				def 						_a      ( self ):
										lowerCamelCase__   =self.tokenizer_class.from_pretrained("microsoft/prophetnet-large-uncased" )

										lowerCamelCase__   =["A long paragraph for summarization.", "Another paragraph for summarization."]
										lowerCamelCase__   =[1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102]
										lowerCamelCase__   =tokenizer(lowercase_      ,					padding=lowercase_      ,					return_tensors="pt" )
										self.assertIsInstance(lowercase_      ,					lowercase_ )
										lowerCamelCase__   =list(batch.input_ids.numpy()[0] )
										self.assertListEqual(lowercase_      ,					lowercase_ )

										self.assertEqual((2, 9)      ,					batch.input_ids.shape )
										self.assertEqual((2, 9)      ,					batch.attention_mask.shape )

				def 						_a      ( self ):
										self.assertTrue(_is_whitespace(" " ) )
										self.assertTrue(_is_whitespace("\t" ) )
										self.assertTrue(_is_whitespace("\r" ) )
										self.assertTrue(_is_whitespace("\n" ) )
										self.assertTrue(_is_whitespace("\u00A0" ) )

										self.assertFalse(_is_whitespace("A" ) )
										self.assertFalse(_is_whitespace("-" ) )

				def 						_a      ( self ):
										self.assertTrue(_is_control("\u0005" ) )

										self.assertFalse(_is_control("A" ) )
										self.assertFalse(_is_control(" " ) )
										self.assertFalse(_is_control("\t" ) )
										self.assertFalse(_is_control("\r" ) )

				def 						_a      ( self ):
										self.assertTrue(_is_punctuation("-" ) )
										self.assertTrue(_is_punctuation("$" ) )
										self.assertTrue(_is_punctuation("`" ) )
										self.assertTrue(_is_punctuation("." ) )

										self.assertFalse(_is_punctuation("A" ) )
										self.assertFalse(_is_punctuation(" " ) )






				@slow
				def 						_a      ( self ):
										lowerCamelCase__   =self.tokenizer_class.from_pretrained("microsoft/prophetnet-large-uncased" )

										lowerCamelCase__   =tokenizer.encode("sequence builders"      ,					add_special_tokens=lowercase_ )
										lowerCamelCase__   =tokenizer.encode("multi-sequence build"      ,					add_special_tokens=lowercase_ )

										lowerCamelCase__   =tokenizer.build_inputs_with_special_tokens(lowercase_ )
										lowerCamelCase__   =tokenizer.build_inputs_with_special_tokens(lowercase_      ,					lowercase_ )

										assert encoded_sentence == text + [102]
										assert encoded_pair == text + [102] + text_a + [102]



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__  =					{
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					['''DebertaTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''DebertaForMaskedLM''',
       '''DebertaForQuestionAnswering''',
       '''DebertaForSequenceClassification''',
       '''DebertaForTokenClassification''',
       '''DebertaModel''',
       '''DebertaPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFDebertaForMaskedLM''',
       '''TFDebertaForQuestionAnswering''',
       '''TFDebertaForSequenceClassification''',
       '''TFDebertaForTokenClassification''',
       '''TFDebertaModel''',
       '''TFDebertaPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
   from .tokenization_deberta import DebertaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_deberta_fast import DebertaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_deberta import (
          DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          DebertaForMaskedLM,
          DebertaForQuestionAnswering,
          DebertaForSequenceClassification,
          DebertaForTokenClassification,
          DebertaModel,
          DebertaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_deberta import (
          TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDebertaForMaskedLM,
          TFDebertaForQuestionAnswering,
          TFDebertaForSequenceClassification,
          TFDebertaForTokenClassification,
          TFDebertaModel,
          TFDebertaPreTrainedModel,
      )


else:
   import sys

   lowerCamelCase__  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




